WO2018072482A1 - Procédé et dispositif de traitement de données pour un robot et robot - Google Patents

Procédé et dispositif de traitement de données pour un robot et robot Download PDF

Info

Publication number
WO2018072482A1
WO2018072482A1 PCT/CN2017/091979 CN2017091979W WO2018072482A1 WO 2018072482 A1 WO2018072482 A1 WO 2018072482A1 CN 2017091979 W CN2017091979 W CN 2017091979W WO 2018072482 A1 WO2018072482 A1 WO 2018072482A1
Authority
WO
WIPO (PCT)
Prior art keywords
event
decision
data
determined
obtaining
Prior art date
Application number
PCT/CN2017/091979
Other languages
English (en)
Chinese (zh)
Inventor
刘若鹏
欧阳一村
Original Assignee
深圳光启合众科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳光启合众科技有限公司 filed Critical 深圳光启合众科技有限公司
Publication of WO2018072482A1 publication Critical patent/WO2018072482A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the present invention relates to the field of robots, and in particular to a data processing method and apparatus for a robot, and a robot.
  • Embodiments of the present invention provide a data processing method and apparatus for a robot, and a robot, to at least solve the problem in the prior art that the robot is in a decision, and the robot cannot make a decision because the received information is not comprehensive. technical problem.
  • a data processing method for a robot including: acquiring one or more current state data of an associated event, where the associated event is an event corresponding to the event to be determined; Input one or more current state data into a preset decision model to obtain decision data of the event to be determined; and obtain a decision result of the event to be determined according to the decision data.
  • a data processing apparatus for a robot including: a first acquiring unit, configured to acquire one or more current state data of an associated event, where the associated event An event corresponding to the event to be determined; an input unit, configured to input one or more current state data to a predetermined decision model, to obtain decision data of the event to be determined; and a decision unit, configured to determine
  • the policy data is the result of the decision of the event to be decided.
  • a robot comprising any one of the above embodiments for a data processing apparatus for a robot.
  • the foregoing solution of the present application obtains current state data of an associated event, where the associated event is an associated event corresponding to the event to be determined, and one or more current state data is input to
  • the preset decision model obtains decision data of the event to be determined, inputs one or more current state data into a preset decision model, and obtains decision data of the event to be determined.
  • the above solution obtains the final decision result by inputting the current state data of the associated event of the event to be determined to the preset decision model, and realizes that when the acquired information is not comprehensive, or the acquired multiple information conflicts,
  • the use of other information to allow the robot to make decisions solves the technical problem that the robot is unable to make decisions because the received information is not comprehensive in the prior art.
  • FIG. 1 is a flowchart of a data processing method for a robot according to a first embodiment of the present invention
  • FIG. 2 is a network structure diagram of an associated event according to a first embodiment of the present invention
  • FIG. 3 is a schematic diagram of a data processing apparatus for a robot according to a second embodiment of the present invention.
  • FIG. 4 is a schematic diagram of an optional data processing apparatus for a robot according to a second embodiment of the present invention.
  • FIG. 5 is a schematic diagram of an optional data processing apparatus for a robot according to a second embodiment of the present invention.
  • FIG. 6 is a schematic diagram of an optional data processing apparatus for a robot according to a second embodiment of the present invention.
  • FIG. 7 is a schematic diagram of an optional data processing apparatus for a robot according to a second embodiment of the present invention. Figure.
  • a method embodiment of a data processing method for a robot is provided, it being noted that the steps illustrated in the flowchart of the figures may be in a set of computer executable instructions, such as The steps are performed in a computer system, and although the logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than the ones described herein.
  • FIG. 1 is a data processing method for a robot according to a first embodiment of the present invention. As shown in FIG. 1, the method includes the following steps:
  • Step S102 Acquire one or more current state data of the associated event, where the associated event is an event corresponding to the event to be determined.
  • the associated event is used as an event corresponding to the event to be determined, and is used to indicate an event that is associated with the perceived event, for example, may be different according to a result of the event to be determined. Events in different states may also be events that have a certain state at a certain moment, but have different decision-making outcomes in different states.
  • the robot determines whether the character of the trick is a master.
  • the associated event may be the date of the trick, the date of the trick, and the example. A trick in the daytime.
  • the robot still determines whether the character of the trick is a master.
  • the associated event may be, after the person closes the door, whether the door is closed or not. Send a password to the robot, go directly to the bedroom after the door is closed.
  • any of the associated events are events that have a logical relationship with the event to be determined, that is, the result of the decision event is affected, or may be the result of the event to be decided.
  • the impact is an event.
  • Step S104 Input one or more current state data to a preset decision model to obtain decision data of the event to be determined.
  • the decision model may be based on a person's tricks and a decision model obtained from the habit of the associated event related to the trick event.
  • any branch of the associated event in the decision model is directly or indirectly related to the event to be determined, that is, the state of any associated event in the decision model is obtained, and the decision can be made.
  • the state of the multiple associated events is obtained, and the decision result of the event to be decided can also be obtained.
  • the above decision data may be a probability value or a binarized data, which may be used to indicate the possibility that the decision to be made is under one or more decision results.
  • the robot still determines whether the person of the trick is a master, and the sound data detected by the robot is similar to the voice data of the master, but the detected image data conflicts with each other. It is impossible to judge whether the character of the trick is a master. Therefore, it is detected whether the trick person is a related event associated with the event of the master. For example, after the robot detects the character, the password is sent to the person, and the character is ⁇ The password is sent to the robot after the door. This state is input to the preset decision model to obtain the decision result. The current state data of the remaining associated events can also be input to the preset decision model.
  • Step S106 Obtain a decision result of the event to be determined according to the decision data.
  • the decision data may be, for example, 0.153, 0.713.
  • the probability value, and the result of the decision may be, for example, "the character of the trick is the master", "1", etc., based on the decision data.
  • the foregoing step of the present application acquires current state data of the associated event, where the associated event is an associated event corresponding to the event to be determined, and one or more current state data is input to a preset decision model, and is obtained.
  • the decision data of the decision event is based on the decision data to obtain the decision result of the event to be decided.
  • the above solution obtains the final decision result by inputting the current state data of the associated event of the event to be determined to the preset decision model, and realizes that when the acquired information is not comprehensive, or the acquired multiple information conflicts,
  • the use of other information to allow the robot to make decisions solves the technical problem that the robot is unable to make decisions because the received information is not comprehensive in the prior art.
  • the method before the one or more current state data is input to the preset decision model to obtain the decision data of the event to be determined, the method further includes: acquiring the preset Decision parameters of the decision model, wherein the decision parameters of the preset decision model are obtained.
  • Step S1081 Obtain a network structure of the event to be determined.
  • the network structure may be a network structure formed according to priorities of multiple associated events and influence relationships between them.
  • the robot is still determined by the robot as the master of the trick, and the associated event is:
  • M Whether the target is a master, True or False
  • DC Whether a closing event is observed in a period of time after the door, True or False
  • BR Whether the target enters the bedroom directly (from the sound, or the image cannot be immediately tracked to the target), True or False.
  • the above associated event is represented in the format of "code: associated event, state selectable value", in an optional real
  • the robot is still determined by the above-mentioned robot as a master.
  • the network structure of the event may be a network structure as shown in FIG. 2, and the M event is a pending event.
  • the event WE and the event DO are trigger events of the event M, that is, the state data of the event WE and the event DO have an influence relationship with the event M, and the event M is the event DC, BR,
  • the trigger event of OR that is, the result of event M (whether the character of the trick is the master) affects the status data of the above three events, and the influence relationship between the above six events is combined with the arrow pointing to FIG.
  • the event WE and the event DO are not mutually independent events, wherein the probability that the character is slamming after six o'clock is affected by whether it is a probability value of the weekend, in this case before the working day before six o'clock.
  • the probability that the character of the Tuen Mun is the owner is extremely low at 0.17, and when the event occurs on the weekend, the probability that the two intervening segments are the master's voice is equal before and after the six o'clock, so the event WE and the event DO are interrelated, and
  • the non-independent events associated with the event M wherein the data given in Table 1 is the transition probability between the event WE and the event DO.
  • Step S1083 Acquire historical state data of the associated event and historical result data corresponding to the historical state data.
  • the historical state data of the associated event and the historical result corresponding to the historical state data may be empirical values of the associated event, and the more empirical values obtained, the higher the accuracy of the obtained decision model.
  • Step S1085 Obtain an influence factor of the current state data of any associated event or adjacent multiple associated events in the network structure to be affected by the decision event according to the historical state data and the historical result data.
  • Step S1087 confirming that the impact factor is a decision parameter.
  • the impact factor is the influence factor of the state data of any associated event or multiple associated events to the decision event, after obtaining the impact factor, the state of any associated event can be obtained. Get the impact factor on the decision event.
  • the foregoing steps of the present application acquire the network structure of the event to be determined, obtain the historical state data of the associated event, and the historical result corresponding to the historical state data, and obtain the network structure according to the historical state data and the historical result data.
  • the event data of the event or adjacent events is the influence factor of the decision event, and the decision model is formed by the network structure and the influence factor.
  • the above scheme provides a method for constructing a preset decision model, and by influencing factors on historical state data and historical results of related events, The decision parameters corresponding to the decision event are formed.
  • the scheme uses the historical state data and the historical result of the associated event, the relationship between the state data of the associated event and the result is obtained, and the experience is applied to obtain the decision parameter, so that the decision model
  • the decision parameters used have an empirical basis, so the accuracy of the decision model can be guaranteed, thereby solving the technical problem in the prior art that the robot is unable to make decisions because the received information is not comprehensive.
  • the impact factors of the current state data of any event or adjacent multiple events in the network structure to be affected by the decision event are:
  • Step S1089 Input historical state data and historical result data to a preset network model.
  • Step S1091 Obtain an impact factor of the preset network model output, where the impact factor includes at least a probability value of any current state data corresponding to different decision results.
  • the above probability value may be a transition probability value.
  • the robot is still determined by the robot as a master, and based on the network structure of the event to be determined, the relationship between the associated event and the event to be determined is obtained, as shown in Table 1 to As shown in Table 5, the association relationship is the transition probability between events, and is obtained from training history state data and results.
  • the value 0.5 is the probability value of the event WE "TRUE" ⁇
  • the event DO takes the value of "before”
  • the values in Tables 1 to 5 are used to indicate the transition probability, where Since the event WE and the event DO are used to determine the decision event, the first table is only the event WE and the event DO.
  • the transition probability between the two, Table 2 is the transition probability of the event WE and the event DO and the event to be decided.
  • the foregoing steps of the present application input historical state data and historical result data into a preset network model, and obtain an impact factor of the preset network model output; wherein the impact factor includes at least any state data corresponding to different decisions.
  • the probability value of the result uses historical state data and historical results as parameters for obtaining impact factors, so that the impact factors are obtained based on historical "experience", which ensures the accuracy of the influence factors and thus ensures the accuracy of the decision model.
  • acquiring a network structure of the event to be determined includes:
  • Step S1093 Acquire a priority of an associated event corresponding to the event to be determined.
  • the foregoing priority is used to represent the degree of influence of the associated event on the decision event, and the higher the impact of the associated event on the decision event, the higher the priority;
  • the foregoing priority can also be used to represent the stability of the impact of the associated event on the decision event, that is, the associated event is in the same state, and the event to be determined is correspondingly a corresponding result, and the above stability is higher. The higher the priority of the associated event.
  • Step S1095 The network structure is constructed according to the priority.
  • the event WE has the highest priority, and the events DC, BR, and OR are equally prioritized, and it can be considered that There is no necessary relationship between events DC, BR, OR, but they all have a direct relationship with event M.
  • the above steps of the present application acquire the priority of the associated event corresponding to the event to be determined, and construct the network structure according to the priority.
  • the above solution obtains the network structure of the event to be determined through the priority of multiple associated events, constructs the association relationship between the event to be determined and multiple related events, and provides a network structure for the construction of the decision model, and ensures the priority by means. The accuracy of the decision model.
  • acquiring current state data of the associated event includes:
  • Step S1021 Acquire a current state of an associated event corresponding to the event to be determined.
  • Step S1023 Find a current state in a preset state area.
  • Step S1025 Confirm that the status data corresponding to the status area to which the status belongs is the current status data of the associated event.
  • the foregoing step of the present application acquires the current state of the associated event corresponding to the event to be determined, searches for the state in the preset state region, and confirms that the state data corresponding to the state region to which the state belongs is associated.
  • the current state data of the event realizes the technical effect of obtaining the decision result through the decision data, thereby solving the technical problem that the robot cannot make the decision because the received information is not comprehensive in the prior art.
  • the decision result of the event to be determined is obtained according to the decision data, including:
  • Step S1097 Obtain a preset decision interval and a decision result corresponding to the preset decision interval.
  • NODE represents different nodes, that is, different associated data
  • VALUE is used to represent status data of an event, including False (indicating that an event has not occurred), Tm. e (indicating that the event occurred), after (the trick event occurred after the preset time) and before (the trick event occurred before the preset time)
  • MARGIAL is used to characterize the decision data, using the above decision model, not entered Any state data, that is, the state data of any associated event, cannot be determined, and the decision data as shown in Table 6 can be obtained, wherein the event M is the data to be determined, and the probability value corresponding to the event M is the decision of the event to be decided.
  • the probability that the person of the door is the master is 0.633214, the character of the door is not the master.
  • the probability is 0.366786.
  • the robot still receives status data for a plurality of associated events, unlike the previous embodiment, in this embodiment
  • the robot detects a closing event in a period of time after the detection of the door, that is, the event DC is different from the previous embodiment. Due to the influence of the event DC, the final decision data is different from the previous embodiment.
  • the probability that the character of the trick is the owner is 0.952345, and the probability of not being the owner is 0.0476 55
  • Step S1099 Confirming that the decision result corresponding to the decision interval to which the decision data belongs is the decision result of the event to be decided.
  • the application scenario is still taken as an example, and may be divided into two decision intervals, where the first decision interval is (0, 0.499999), and the character used to represent the trick is the owner,
  • the second decision interval is [0. 499999.1), which is used to indicate that the character of the trick is not the owner.
  • the foregoing steps of the present application obtain the decision result corresponding to the preset decision interval and the preset decision interval, and confirm that the decision result corresponding to the decision interval to which the decision data belongs is the decision result of the event to be decided.
  • the above solution achieves the technical purpose of obtaining decision results through decision data.
  • the preset decision model is a Bayesian network model.
  • the Bayesian network is a mathematical model based on probabilistic reasoning. Based on the Bayesian formula, the probability inference is to obtain the composition of other probability information through the information of some variables, which is used to solve Problems caused by uncertainty or correlation between equipment or equipment. In this application, the correlation between the associated event and the decision to be made is used to make the decision. [0080] In an optional embodiment, the robot still determines whether the character of the trick is a master. After obtaining the decision parameter, the state data of the obtained associated event is input to the preset Bayes. The network model (or Bayesian formula) can be used to obtain decision data.
  • an apparatus embodiment of a data processing apparatus for a robot is provided.
  • FIG. 3 is a schematic diagram of a data processing apparatus for a robot according to a second embodiment of the present invention. As shown in FIG. 3, the apparatus includes:
  • the first obtaining unit 30 is configured to acquire one or more current state data of the associated event, where the associated event is an event corresponding to the event to be determined.
  • the associated event is used as an event corresponding to the event to be determined, and is used to indicate an event that is associated with the perceived event, for example, may be different according to a result of the event to be determined.
  • Events in different states may also be events that have a certain state at a certain moment, but have different decision-making outcomes in different states.
  • any of the associated events are events that have a logical relationship with the event to be determined, that is, the result of the decision event is affected, or may be the result of the event to be decided.
  • the impact is an event.
  • the input unit 32 is configured to input one or more current state data to a preset decision model to obtain decision data of the event to be decided.
  • the decision model may be based on a person's tricks and a decision model obtained from the habit of an associated event related to the Tuen Mun event.
  • any branch of the associated event in the decision model is directly or indirectly related to the event to be determined, that is, the state of any associated event in the decision model is obtained, and the decision can be made.
  • the state of the multiple associated events is obtained, and the decision result of the event to be decided can also be obtained.
  • the above decision data may be a probability value or a binarized data, and may be used to indicate the possibility that the decision to be made is under one or more decision results.
  • the determining unit 34 is configured to obtain a decision result of the event to be determined according to the decision data.
  • the foregoing device of the present application acquires current state data of the associated event by using the first acquiring unit 30, wherein the associated event is an associated event corresponding to the event to be determined, and one or more current state data are input through the input unit 32.
  • the decision data is input to the preset decision model, and the decision data of the event to be determined is obtained by the decision unit 34 according to the decision data.
  • the above solution obtains the final decision result by inputting the current state data of the associated event of the event to be determined to the preset decision model, and realizes that when the acquired information is not comprehensive, or the acquired multiple information conflicts,
  • the use of other information to allow the robot to make decisions solves the technical problem that the robot is unable to make decisions because the received information is not comprehensive in the prior art.
  • the foregoing apparatus further includes a second acquiring unit 40, configured to acquire a decision parameter of the decision model, where the second obtaining unit 40 includes:
  • the first obtaining module 42 is configured to acquire a network structure of an event to be determined.
  • the second obtaining module 44 is configured to acquire historical state data of the associated event and historical result data corresponding to the historical state data.
  • the first confirmation module 46 is configured to obtain, according to the historical state data and the historical result data, an influence factor of the status data of any associated event or adjacent multiple associated events in the network structure to the decision event.
  • the second confirmation module 48 is configured to confirm that the impact factor is a decision parameter.
  • the impact factor is the influence factor of the state data of any associated event or multiple associated events to the decision event, after obtaining the impact factor, the state of any associated event can be obtained. Get the impact factor on the decision event.
  • the foregoing apparatus of the present application acquires a network structure of a to-be-determined event by using the first acquiring module, and acquires historical state data of the associated event and historical result corresponding to the historical state data by using the second obtaining module, and passes the first confirmation module.
  • the influence factors of the status data of any event or adjacent events in the network structure are determined, and the second confirmation module is used to confirm that the impact factor is a decision parameter.
  • the above solution provides a method for constructing a preset decision model, and obtains an influence factor on the historical state data and historical results of the relevant event, and then forms a decision parameter corresponding to the decision event, because the scheme uses the historical state data of the associated event and Historical results, thereby obtaining the correlation between the state data of the associated event and the result, applying the experience to the acquisition decision
  • the parameters make the decision parameters used by the decision model have an empirical basis, thus ensuring the accuracy of the decision model, thereby solving the problem in the prior art that the robot is making decisions, and the robot cannot make decisions because the received information is not comprehensive. technical problem.
  • the first confirmation module 46 includes:
  • the input sub-module 50 is configured to input historical state data and historical result data to a preset network model; [0102] an obtaining sub-module 52, configured to acquire an impact factor of the preset network model output; wherein, the impact The factor includes at least the probability value of any current state data corresponding to different decision outcomes.
  • the foregoing apparatus of the present application obtains an influence factor of a preset network model output by acquiring a history state data and historical result data into a preset network model, where the influence factor includes at least an arbitrary factor.
  • the status data corresponds to the probability values of different decision outcomes.
  • the above scheme uses historical state data and historical results as parameters to obtain impact factors, so that the impact factors are obtained based on historical "experience", which ensures the accuracy of the impact factors, thus ensuring the accuracy of the decision model.
  • the first obtaining module 42 includes:
  • the obtaining sub-module 60 is configured to acquire a priority of an associated event corresponding to the event to be determined.
  • the construction submodule 62 is configured to construct a network structure according to priorities.
  • the foregoing apparatus of the present application acquires the priority of the associated event corresponding to the event to be determined by acquiring the submodule, and constructs the network structure according to the priority by constructing the submodule.
  • the above solution obtains the network structure of the event to be determined through the priority of multiple associated events, constructs the association relationship between the event to be determined and multiple related events, and provides a network structure for the construction of the decision model, and ensures the priority by means. The accuracy of the decision model.
  • the first obtaining unit 30 includes:
  • the third obtaining module 70 is configured to acquire a current state of the associated event corresponding to the event to be determined.
  • the searching module 72 is configured to search for a current state in a preset state area.
  • the third confirmation module 74 is configured to confirm that the status data corresponding to the status area to which the status belongs is the current status data of the associated event.
  • the device of the present application acquires the current state of the associated event corresponding to the event to be determined through the third acquiring module, searches for the state in the preset state region by the searching module, and confirms the state by the third confirming module 74.
  • the status data corresponding to the associated status area is the current status data of the associated event.
  • the preset decision model is a Bayesian network model.
  • the Bayesian network is a mathematical model based on probabilistic reasoning. Based on the Bayesian formula, the probability inference is to obtain the composition of other probability information through the information of some variables, which is used to solve Problems caused by uncertainty or correlation between equipment or equipment. In this application, the correlation between the related event and the pending policy is used to make the decision.
  • the robot still determines whether the character of the trick is a master. After obtaining the decision parameter, the state data of the obtained associated event is input to the preset Bayes.
  • the network model (or Bayesian formula) can be used to obtain decision data.
  • a robot comprising the data processing apparatus for a robot of any one of the second embodiments.
  • the robot provided in the third embodiment can perform the event determination by the data processing device for the robot.
  • the device for the data processing device of the robot provided by the second embodiment of the present application acquires the associated event by using the first acquiring unit 30.
  • the current state data, wherein the associated event is an associated event corresponding to the event to be determined, and the input unit 32 inputs one or more current state data to a preset decision model, and obtains decision data of the event to be determined, by the decision unit 34.
  • the decision result of the event to be decided is obtained according to the decision data.
  • the above solution obtains the final decision result by inputting the current state data of the associated event of the event to be determined to the preset decision model, and realizes that when the acquired information is not comprehensive, or the acquired multiple information conflicts,
  • the use of other information to allow the robot to make decisions solves the technical problem that the robot is unable to make decisions because the received information is not comprehensive in the prior art.
  • the disclosed technical content may be It's way to achieve it.
  • the device embodiments described above are only schematic.
  • the division of the unit may be a logical function division.
  • the actual implementation may have another division manner.
  • multiple units or components may be combined or may be Integration into another system, or some features can be ignored, or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, unit or module, and may be electrical or otherwise.
  • the unit described as a separate component may or may not be physically distributed, and the component displayed as a unit may or may not be a physical unit, that is, may be located in one place, or may be distributed to multiple On the unit. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiment of the present embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the technical solution of the present invention may contribute to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of the various embodiments of the present invention.
  • the foregoing storage medium includes: a USB flash drive, a read only memory (ROM, Read-Only)
  • RAM Random Access Memory
  • removable hard disk disk or optical disk, and other media that can store program code.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Manipulator (AREA)

Abstract

L'invention porte sur un procédé et sur un dispositif de traitement de données pour un robot, ainsi que sur un robot. Le procédé consiste : à obtenir un ou plusieurs éléments de données de l'état actuel d'un événement associé (S102), l'événement associé étant un événement correspondant à un événement à décider ; à entrer le ou les éléments de données de l'état actuel dans un modèle de décision préétabli afin d'obtenir des données de décision de l'événement à décider (S104) ; à obtenir un résultat de décision de l'événement à décider en fonction des données de décision (S106). La présente invention résout le problème technique dans l'état de la technique selon lequel un robot est incapable de prendre une décision, en raison d'informations incomplètes qui sont reçues lorsque le robot prend une décision.
PCT/CN2017/091979 2016-10-18 2017-07-06 Procédé et dispositif de traitement de données pour un robot et robot WO2018072482A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610907004.7 2016-10-18
CN201610907004.7A CN107958289B (zh) 2016-10-18 2016-10-18 用于机器人的数据处理方法和装置、机器人

Publications (1)

Publication Number Publication Date
WO2018072482A1 true WO2018072482A1 (fr) 2018-04-26

Family

ID=61954487

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/091979 WO2018072482A1 (fr) 2016-10-18 2017-07-06 Procédé et dispositif de traitement de données pour un robot et robot

Country Status (2)

Country Link
CN (1) CN107958289B (fr)
WO (1) WO2018072482A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111775159A (zh) * 2020-06-08 2020-10-16 华南师范大学 基于动态人工智能伦理规则的伦理风险防范方法和机器人
CN117387649A (zh) * 2023-10-26 2024-01-12 苏州大学 概率自更新的不确定环境机器人自适应导航方法及系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101105845A (zh) * 2006-06-07 2008-01-16 索尼株式会社 信息处理装置和信息处理方法、以及计算机程序
CN104346341A (zh) * 2013-07-24 2015-02-11 腾讯科技（深圳）有限公司 一种实现数据与相关事件关联的方法及装置
CN104680031A (zh) * 2015-03-18 2015-06-03 联想(北京)有限公司 一种联动规则生成方法和装置
CN105574350A (zh) * 2015-12-30 2016-05-11 北京锐安科技有限公司 事件预测方法

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7899060B2 (en) * 2004-04-01 2011-03-01 Nortel Networks Limited Method for providing bearer specific information for wireless networks
CN101055630A (zh) * 2006-04-12 2007-10-17 科凌力医学软件（深圳）有限公司 事件决策知识库组建方法及相应的事件决策方法和系统
CN101282342B (zh) * 2008-05-30 2012-05-23 腾讯科技（深圳）有限公司 一种网络内容拉取方法与系统
CN101807227A (zh) * 2010-01-13 2010-08-18 中国电子科技集团公司第五十四研究所 一种常规设施目标毁伤效果的计算方法
CN101923561A (zh) * 2010-05-24 2010-12-22 中国科学技术信息研究所 一种文件自动分类方法
WO2013109082A1 (fr) * 2012-01-20 2013-07-25 삼성전자 주식회사 Procédé et dispositif pour la définition d'une priorité dans la transmission de données
CN102693498A (zh) * 2012-05-16 2012-09-26 上海卓达信息技术有限公司 一种基于不完善数据的精准推荐方法
CN103166819B (zh) * 2013-03-07 2016-04-20 南京邮电大学 一种基于业务优先级的网络结构及其推送方法
CN103885788B (zh) * 2014-04-14 2015-02-18 焦点科技股份有限公司 一种基于模型组件化动态web 3d虚拟现实场景的搭建方法及系统
CN104090573B (zh) * 2014-06-27 2017-01-25 赵希源 一种基于蚁群算法的机器人足球动态决策装置及其方法
CN105184386A (zh) * 2015-07-22 2015-12-23 中国寰球工程公司 一种结合专家经验和历史数据建立异常事件预警系统的方法
CN105490858B (zh) * 2015-12-15 2018-08-03 北京理工大学 一种网络结构的动态链路预测方法
CN205510078U (zh) * 2016-03-31 2016-08-24 深圳光启合众科技有限公司 群体广播信息发送装置
CN105930924B (zh) * 2016-04-15 2021-03-02 中国电力科学研究院 基于复杂事件处理技术和决策树的配电网态势感知方法
CN105975797B (zh) * 2016-05-27 2019-01-25 北京航空航天大学 一种基于模糊数据处理的产品早期故障根原因识别方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101105845A (zh) * 2006-06-07 2008-01-16 索尼株式会社 信息处理装置和信息处理方法、以及计算机程序
CN104346341A (zh) * 2013-07-24 2015-02-11 腾讯科技（深圳）有限公司 一种实现数据与相关事件关联的方法及装置
CN104680031A (zh) * 2015-03-18 2015-06-03 联想(北京)有限公司 一种联动规则生成方法和装置
CN105574350A (zh) * 2015-12-30 2016-05-11 北京锐安科技有限公司 事件预测方法

Also Published As

Publication number Publication date
CN107958289B (zh) 2022-02-01
CN107958289A (zh) 2018-04-24

Similar Documents

Publication Publication Date Title
US11551103B2 (en) Data-driven activity prediction
US9202173B1 (en) Using link analysis in adversarial knowledge-based authentication model
US20180137001A1 (en) Discovering critical alerts through learning over heterogeneous temporal graphs
US9590966B2 (en) Reducing authentication confidence over time based on user history
US20160203316A1 (en) Activity model for detecting suspicious user activity
CN107438845A (zh) 基于屏幕分析的设备安全性
CN105635654B (zh) 视频监控方法、装置及系统、摄像机
US10650273B2 (en) Face recognition in a residential environment
Ristić et al. A mixed INAR (p) model
CN114462090B (zh) 一种针对联邦学习中差分隐私预算计算的收紧方法
CN105637522A (zh) 使用受信证书的世界驱动访问控制
WO2018120962A1 (fr) Système de reconnaissance de contexte avec élimination d'incertitude basé sur la gestion de fiabilité et son procédé de fonctionnement
CN110839031A (zh) 一种基于强化学习的恶意用户行为智能检测方法
CN109326025A (zh) 智能门锁的开锁方法及装置
EP3471060A1 (fr) Appareil et procédés de détermination et de fourniture de contenu anonymisé dans des images
WO2018072482A1 (fr) Procédé et dispositif de traitement de données pour un robot et robot
US20210397823A1 (en) Computerized system and method for adaptive stranger detection
TWI728285B (zh) 身分識別的方法、裝置、系統、伺服器及電腦可讀儲存媒體
CN112002075B (zh) 一种提升储物柜安全性的信息处理方法和装置
CN112087435B (zh) 分层上下文认知决策方法、装置、电子设备及存储介质
EP3791296A1 (fr) Système et procédé de révélation d'anomalies séquentielles dans un réseau informatique
CN109255016A (zh) 基于深度学习的应答方法、装置及计算机可读存储介质
Nguyen et al. A trust-based mechanism for avoiding liars in referring of reputation in multiagent system
Wan et al. Detecting masqueraders using high frequency commands as signatures
US9183595B1 (en) Using link strength in knowledge-based authentication

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17861692

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17861692

Country of ref document: EP

Kind code of ref document: A1